[Deep Learning Lab] Episode-1: Fashion-MNIST

This is the first episode of “Deep Learning Lab” story series which contains my individual works for deep learning with different cases.

The dataset for the first episode that I would like to work on is MNIST dataset -not surprisingly-. However, it is not MNIST handwritten digit database as first come to your mind, but MNIST-like fashion product database. Actually, Fashion-MNIST -wow!-.

Fashion-MNIST

Fashion-MNIST dataset has been developed by the Zalando Research Team as clothes product database and as an alternative to the original MNIST handwritten digits database. Besides to have the same physical characteristics as the ancestor (the original one), there are 60.000 images for training a model and 10.000 images for evaluating the performance of the model. The most significant reason for picking this dataset is that the vast majority of searches about deep learning on Google may introduce you to the original MNIST, but you are now probably meeting Fashion-MNIST for the first time -don’t you?-.

MNIST is overused. In this April 2017 Twitter thread, Google Brain research scientist and deep learning expert Ian Goodfellow calls for people to move away from MNIST.

MNIST can not represent modern computer vision tasks.

(Han Xiao et al.)

If I succeeded in convincing you enough for Fashion-MNIST, let’s start coding.

LET’S GOOOOO!

I prefer to use Tensorflow and Keras for my works. In the “Deep Learning Lab” series, I would like to choose Keras, which gives you an opportunity to understand how the code works even if you have minimum/no knowledge of the subject -well, not tell a lie; it needs to know a bit about Python, and also to follow the literature-.

We are now ready to compile our model. The categorical crossentropy function has been picked out as a loss function because we have more than 2 labels and already prepared the labels in the categorical matrix structure.

Our model predicted 90.52% of 10.000 test images as correct. For the literature performances: GO! (You can see under the Benchmark heading)

Well, the first episode of “Deep Learning Lab” series, Fashion-MNIST ends here. Thank you for taking the time with me. For comments and suggestions, please e-mail me. You can also contact me via LinkedIn. Thank you.